In the new era of big data, companies and other organizations have access to vast amounts of structured and unstructured data as well as access to a variety of new data sources. As a result, many data analytics applications have been developed to provide users with insight into their data. One example genre of data analytics applications includes workforce analytics. Workforce analytics applications are used by businesses and other organizations to assist users in understanding their data, making appropriate decisions, and find answers to key questions to gain the insight needed to take actions. Workforce analytics applications are adapted for providing statistical models to worker-related data, allowing companies to optimize their various enterprise processes.
The workforce analytics application may use a cube data structure to respond to queries. To build the cube data structure, several fact tables need to be joined together. Each fact table includes parts of the data for the cube data structure. The database system uses a join to build the complete picture of the data for the cube data structure. To ensure that no data is lost when the join is performed, the database system can perform an outer join. The outer join may still create a record for the cube data structure when a record exists in one table without a corresponding record in another table. However, the outer join is a slow process and when the outer join is performed in real time, a user of the database system may experience slow response times.